Overview

Dataset statistics

Number of variables21
Number of observations179
Missing cells774
Missing cells (%)20.6%
Duplicate rows12
Duplicate rows (%)6.7%
Total size in memory55.9 KiB
Average record size in memory320.0 B

Variable types

Categorical7
DateTime1
Numeric12
Boolean1

Dataset

DescriptionQuality-verified clinical data for JHB_EZIN_025
CreatorHEAT Research Programme
AuthorRP2 Clinical Data Team
URLhttps://github.com/Logic06183/RP2_dataoverview

Variable descriptions

study_sourceStudy identifier
Age (at enrolment)Patient age at study enrollment
SexBiological sex
RaceRacial/ethnic group
enrollment_dateDate of study enrollment
visit_dateDate of clinic visit
primary_datePrimary reference date
study_armStudy treatment arm
study_visitStudy visit number
Antiretroviral Therapy StatusCurrent ART status
BMI (kg/m²)Body Mass Index
weight_kgBody weight in kilograms
height_mHeight in meters
Waist circumference (cm)Waist circumference in centimeters
hip_circumference_cmHip circumference in centimeters
waist_hip_ratioWaist-to-hip ratio
systolic_bp_mmHgSystolic blood pressure
diastolic_bp_mmHgDiastolic blood pressure
heart_rate_bpmHeart rate in beats per minute
Respiratory rate (breaths/min)Respiratory rate
Oxygen saturation (%)Oxygen saturation
body_temperature_celsiusBody temperature in Celsius
CD4 cell count (cells/µL)CD4+ T lymphocyte count
HIV viral load (copies/mL)HIV RNA copies per mL
cd4_percentCD4+ percentage
cd8_count_cells_uLCD8+ T lymphocyte count
cd4_cd8_ratioCD4/CD8 ratio
Hematocrit (%)Hematocrit
hemoglobin_g_dLHemoglobin concentration
White blood cell count (×10³/µL)Total WBC count
Red blood cell count (×10⁶/µL)Total RBC count
Platelet count (×10³/µL)Platelet count
MCV (MEAN CELL VOLUME)Mean corpuscular volume
mch_pgMean corpuscular hemoglobin
mchc_g_dLMean corpuscular hemoglobin concentration
RDWRed cell distribution width
Lymphocyte count (×10⁹/L)Lymphocyte absolute count
Neutrophil count (×10⁹/L)Neutrophil absolute count
Monocyte count (×10⁹/L)Monocyte absolute count
Eosinophil count (×10⁹/L)Eosinophil absolute count
Basophil count (×10⁹/L)Basophil absolute count
lymphocyte_percentLymphocyte percentage
neutrophil_percentNeutrophil percentage
monocyte_percentMonocyte percentage
eosinophil_percentEosinophil percentage
basophil_percentBasophil percentage
ALT (U/L)Alanine aminotransferase
AST (U/L)Aspartate aminotransferase
Alkaline phosphatase (U/L)Alkaline phosphatase
Total bilirubin (mg/dL)Total bilirubin
direct_bilirubin_mg_dLDirect bilirubin
indirect_bilirubin_mg_dLIndirect bilirubin
Albumin (g/dL)Serum albumin
Total protein (g/dL)Total serum protein
ggt_u_LGamma-glutamyl transferase
creatinine_umol_LSerum creatinine (µmol/L)
creatinine_mg_dLSerum creatinine (mg/dL)
creatinine clearanceEstimated creatinine clearance
bun_mg_dLBlood urea nitrogen
urea_mmol_LSerum urea
egfr_ml_minEstimated glomerular filtration rate
Sodium (mEq/L)Serum sodium
Potassium (mEq/L)Serum potassium
chloride_mEq_LSerum chloride
bicarbonate_mEq_LSerum bicarbonate
calcium_mg_dLSerum calcium
magnesium_mg_dLSerum magnesium
phosphate_mg_dLSerum phosphate
total_cholesterol_mg_dLTotal cholesterol
hdl_cholesterol_mg_dLHDL cholesterol
ldl_cholesterol_mg_dLLDL cholesterol
Triglycerides (mg/dL)Triglycerides
vldl_cholesterol_mg_dLVLDL cholesterol
cholesterol_hdl_ratioTotal cholesterol/HDL ratio
fasting_glucose_mmol_LFasting blood glucose (mmol/L)
glucose_mg_dLBlood glucose (mg/dL)
hba1c_percentGlycated hemoglobin
insulin_uIU_mLSerum insulin
lactate_mmol_LBlood lactate
crp_mg_LC-reactive protein
esr_mm_hrErythrocyte sedimentation rate
pt_secondsProthrombin time
inrInternational normalized ratio
aptt_secondsActivated partial thromboplastin time
uric_acid_mg_dLSerum uric acid
ldh_u_LLactate dehydrogenase
ck_u_LCreatine kinase
amylase_u_LSerum amylase
lipase_u_LSerum lipase
climate_daily_mean_tempDaily mean temperature
climate_daily_max_tempDaily maximum temperature
climate_daily_min_tempDaily minimum temperature
climate_temp_anomalyTemperature anomaly from baseline
climate_heat_day_p90Heat day indicator (>90th percentile)
climate_heat_day_p95Heat day indicator (>95th percentile)
climate_heat_stress_indexHeat stress index
climate_humidityRelative humidity
climate_precipitationPrecipitation
climate_seasonSeason
cd4_correction_appliedQuality flag: CD4 corrections applied
final_comprehensive_fix_appliedQuality flag: Comprehensive corrections applied
waist_circ_unit_correction_appliedQuality flag: Waist circumference unit corrected
sa_biomarker_standardsSouth African biomarker reference standards applied

Alerts

study_source has constant value "JHB_EZIN_025"Constant
cd4_correction_applied has constant value "0.0"Constant
final_comprehensive_fix_applied has constant value "1.0"Constant
waist_circ_unit_correction_applied has constant value "False"Constant
sa_biomarker_standards has constant value "1.0"Constant
climate_heat_day_p90 has constant value "0.0"Constant
climate_heat_day_p95 has constant value "0.0"Constant
Dataset has 12 (6.7%) duplicate rowsDuplicates
BMI (kg/m²) is highly overall correlated with weight_kgHigh correlation
climate_daily_max_temp is highly overall correlated with climate_daily_mean_temp and 4 other fieldsHigh correlation
climate_daily_mean_temp is highly overall correlated with climate_daily_max_temp and 3 other fieldsHigh correlation
climate_daily_min_temp is highly overall correlated with climate_daily_max_temp and 3 other fieldsHigh correlation
climate_heat_stress_index is highly overall correlated with climate_daily_max_temp and 4 other fieldsHigh correlation
climate_season is highly overall correlated with climate_daily_max_temp and 4 other fieldsHigh correlation
climate_temp_anomaly is highly overall correlated with climate_daily_max_temp and 2 other fieldsHigh correlation
weight_kg is highly overall correlated with BMI (kg/m²)High correlation
BMI (kg/m²) has 129 (72.1%) missing valuesMissing
weight_kg has 129 (72.1%) missing valuesMissing
height_m has 129 (72.1%) missing valuesMissing
heart_rate_bpm has 129 (72.1%) missing valuesMissing
Respiratory rate (breaths/min) has 129 (72.1%) missing valuesMissing
body_temperature_celsius has 129 (72.1%) missing valuesMissing

Reproduction

Analysis started2025-11-25 05:10:37.598069
Analysis finished2025-11-25 05:10:42.484721
Duration4.89 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

study_source
Categorical

Constant 

Study identifier

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
JHB_EZIN_025
179 

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2148
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJHB_EZIN_025
2nd rowJHB_EZIN_025
3rd rowJHB_EZIN_025
4th rowJHB_EZIN_025
5th rowJHB_EZIN_025

Common Values

ValueCountFrequency (%)
JHB_EZIN_025179
100.0%

Length

2025-11-25T07:10:42.506460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:42.541020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
jhb_ezin_025179
100.0%

Most occurring characters

ValueCountFrequency (%)
_358
16.7%
J179
8.3%
H179
8.3%
B179
8.3%
E179
8.3%
Z179
8.3%
I179
8.3%
N179
8.3%
0179
8.3%
2179
8.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1253
58.3%
Decimal Number537
25.0%
Connector Punctuation358
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
J179
14.3%
H179
14.3%
B179
14.3%
E179
14.3%
Z179
14.3%
I179
14.3%
N179
14.3%
Decimal Number
ValueCountFrequency (%)
0179
33.3%
2179
33.3%
5179
33.3%
Connector Punctuation
ValueCountFrequency (%)
_358
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1253
58.3%
Common895
41.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
J179
14.3%
H179
14.3%
B179
14.3%
E179
14.3%
Z179
14.3%
I179
14.3%
N179
14.3%
Common
ValueCountFrequency (%)
_358
40.0%
0179
20.0%
2179
20.0%
5179
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_358
16.7%
J179
8.3%
H179
8.3%
B179
8.3%
E179
8.3%
Z179
8.3%
I179
8.3%
N179
8.3%
0179
8.3%
2179
8.3%

primary_date
Date

Primary reference date

Distinct86
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2020-09-08 00:00:00
Maximum2021-07-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-25T07:10:42.576208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:42.623428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BMI (kg/m²)
Real number (ℝ)

High correlation  Missing 

Body Mass Index

Distinct50
Distinct (%)100.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean26.533794
Minimum17.50639
Maximum44.88645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:10:42.668912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum17.50639
5-th percentile18.452728
Q120.78304
median26.201519
Q331.251109
95-th percentile37.077056
Maximum44.88645
Range27.38006
Interquartile range (IQR)10.468069

Descriptive statistics

Standard deviation6.2062204
Coefficient of variation (CV)0.23389872
Kurtosis0.15607854
Mean26.533794
Median Absolute Deviation (MAD)5.3291005
Skewness0.61774767
Sum1326.6897
Variance38.517172
MonotonicityNot monotonic
2025-11-25T07:10:42.714319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.577037251
 
0.6%
38.285672811
 
0.6%
18.185505681
 
0.6%
33.608396091
 
0.6%
21.254018291
 
0.6%
28.833153061
 
0.6%
25.683116171
 
0.6%
22.21074381
 
0.6%
32.510274321
 
0.6%
31.992171331
 
0.6%
Other values (40)40
 
22.3%
(Missing)129
72.1%
ValueCountFrequency (%)
17.506389831
0.6%
18.185505681
0.6%
18.351020411
0.6%
18.577037251
0.6%
19.34523811
0.6%
19.434400831
0.6%
19.486961451
0.6%
19.829481891
0.6%
20.130457851
0.6%
20.189072261
0.6%
ValueCountFrequency (%)
44.886450241
0.6%
39.10156251
0.6%
38.285672811
0.6%
35.59985761
0.6%
33.608396091
0.6%
32.841490141
0.6%
32.637629721
0.6%
32.510274321
0.6%
32.127362041
0.6%
31.992171331
0.6%

weight_kg
Real number (ℝ)

High correlation  Missing 

Body weight in kilograms

Distinct47
Distinct (%)94.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean74.736
Minimum49.9
Maximum117.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:10:42.759958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum49.9
5-th percentile50.85
Q160.55
median73.1
Q384.9
95-th percentile107.7
Maximum117.8
Range67.9
Interquartile range (IQR)24.35

Descriptive statistics

Standard deviation17.108761
Coefficient of variation (CV)0.22892262
Kurtosis-0.14569641
Mean74.736
Median Absolute Deviation (MAD)12.05
Skewness0.57554181
Sum3736.8
Variance292.7097
MonotonicityNot monotonic
2025-11-25T07:10:42.804905image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
902
 
1.1%
70.92
 
1.1%
73.12
 
1.1%
58.21
 
0.6%
83.91
 
0.6%
68.11
 
0.6%
85.31
 
0.6%
75.11
 
0.6%
68.81
 
0.6%
97.31
 
0.6%
Other values (37)37
 
20.7%
(Missing)129
72.1%
ValueCountFrequency (%)
49.91
0.6%
501
0.6%
50.41
0.6%
51.41
0.6%
53.81
0.6%
54.51
0.6%
54.61
0.6%
551
0.6%
56.21
0.6%
58.21
0.6%
ValueCountFrequency (%)
117.81
0.6%
112.41
0.6%
109.51
0.6%
105.51
0.6%
100.11
0.6%
97.31
0.6%
91.91
0.6%
902
1.1%
89.61
0.6%
88.41
0.6%

height_m
Real number (ℝ)

Missing 

Height in meters

Distinct26
Distinct (%)52.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean1.681
Minimum1.52
Maximum1.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:10:42.847430image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.52
5-th percentile1.559
Q11.62
median1.68
Q31.75
95-th percentile1.79
Maximum1.87
Range0.35
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.080311892
Coefficient of variation (CV)0.047776259
Kurtosis-0.52690869
Mean1.681
Median Absolute Deviation (MAD)0.07
Skewness0.19250412
Sum84.05
Variance0.00645
MonotonicityNot monotonic
2025-11-25T07:10:42.887690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1.754
 
2.2%
1.614
 
2.2%
1.724
 
2.2%
1.793
 
1.7%
1.643
 
1.7%
1.653
 
1.7%
1.683
 
1.7%
1.762
 
1.1%
1.632
 
1.1%
1.772
 
1.1%
Other values (16)20
 
11.2%
(Missing)129
72.1%
ValueCountFrequency (%)
1.521
 
0.6%
1.552
1.1%
1.571
 
0.6%
1.582
1.1%
1.591
 
0.6%
1.61
 
0.6%
1.614
2.2%
1.622
1.1%
1.632
1.1%
1.643
1.7%
ValueCountFrequency (%)
1.871
 
0.6%
1.851
 
0.6%
1.793
1.7%
1.781
 
0.6%
1.772
1.1%
1.762
1.1%
1.754
2.2%
1.731
 
0.6%
1.724
2.2%
1.711
 
0.6%

heart_rate_bpm
Real number (ℝ)

Missing 

Heart rate in beats per minute

Distinct34
Distinct (%)68.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean78
Minimum50
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:10:42.927374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile55.45
Q170.25
median76
Q386
95-th percentile102.2
Maximum110
Range60
Interquartile range (IQR)15.75

Descriptive statistics

Standard deviation13.810673
Coefficient of variation (CV)0.17705991
Kurtosis-0.15961177
Mean78
Median Absolute Deviation (MAD)9
Skewness0.20889091
Sum3900
Variance190.73469
MonotonicityNot monotonic
2025-11-25T07:10:43.047549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
744
 
2.2%
673
 
1.7%
723
 
1.7%
832
 
1.1%
762
 
1.1%
862
 
1.1%
732
 
1.1%
822
 
1.1%
802
 
1.1%
642
 
1.1%
Other values (24)26
 
14.5%
(Missing)129
72.1%
ValueCountFrequency (%)
501
 
0.6%
511
 
0.6%
551
 
0.6%
561
 
0.6%
601
 
0.6%
631
 
0.6%
642
1.1%
661
 
0.6%
673
1.7%
701
 
0.6%
ValueCountFrequency (%)
1101
0.6%
1051
0.6%
1041
0.6%
1001
0.6%
991
0.6%
962
1.1%
941
0.6%
911
0.6%
891
0.6%
881
0.6%

Respiratory rate (breaths/min)
Real number (ℝ)

Missing 

Respiratory rate

Distinct8
Distinct (%)16.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean17.94
Minimum14
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:10:43.084926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14.9
Q116
median18
Q319
95-th percentile21
Maximum22
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8888502
Coefficient of variation (CV)0.10528708
Kurtosis-0.05870419
Mean17.94
Median Absolute Deviation (MAD)1.5
Skewness-0.023727412
Sum897
Variance3.5677551
MonotonicityNot monotonic
2025-11-25T07:10:43.121022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1820
 
11.2%
1611
 
6.1%
207
 
3.9%
194
 
2.2%
143
 
1.7%
222
 
1.1%
212
 
1.1%
171
 
0.6%
(Missing)129
72.1%
ValueCountFrequency (%)
143
 
1.7%
1611
6.1%
171
 
0.6%
1820
11.2%
194
 
2.2%
207
 
3.9%
212
 
1.1%
222
 
1.1%
ValueCountFrequency (%)
222
 
1.1%
212
 
1.1%
207
 
3.9%
194
 
2.2%
1820
11.2%
171
 
0.6%
1611
6.1%
143
 
1.7%

body_temperature_celsius
Real number (ℝ)

Missing 

Body temperature in Celsius

Distinct17
Distinct (%)34.0%
Missing129
Missing (%)72.1%
Infinite0
Infinite (%)0.0%
Mean36.488
Minimum35.2
Maximum37.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:10:43.158466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum35.2
5-th percentile35.9
Q136.2
median36.45
Q336.7
95-th percentile37.265
Maximum37.7
Range2.5
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.46582821
Coefficient of variation (CV)0.012766614
Kurtosis0.904937
Mean36.488
Median Absolute Deviation (MAD)0.25
Skewness0.028784237
Sum1824.4
Variance0.21699592
MonotonicityNot monotonic
2025-11-25T07:10:43.194759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
36.47
 
3.9%
36.77
 
3.9%
36.55
 
2.8%
36.34
 
2.2%
364
 
2.2%
36.13
 
1.7%
36.83
 
1.7%
36.23
 
1.7%
373
 
1.7%
37.42
 
1.1%
Other values (7)9
 
5.0%
(Missing)129
72.1%
ValueCountFrequency (%)
35.21
 
0.6%
35.51
 
0.6%
35.92
 
1.1%
364
2.2%
36.13
1.7%
36.23
1.7%
36.34
2.2%
36.47
3.9%
36.55
2.8%
36.61
 
0.6%
ValueCountFrequency (%)
37.71
 
0.6%
37.42
 
1.1%
37.12
 
1.1%
373
1.7%
36.91
 
0.6%
36.83
1.7%
36.77
3.9%
36.61
 
0.6%
36.55
2.8%
36.47
3.9%

Potassium (mEq/L)
Real number (ℝ)

Serum potassium

Distinct30
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8128492
Minimum3.5
Maximum6.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:10:43.231218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile3.9
Q14.4
median4.8
Q35.1
95-th percentile5.9
Maximum6.6
Range3.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.60670619
Coefficient of variation (CV)0.12605967
Kurtosis0.32414825
Mean4.8128492
Median Absolute Deviation (MAD)0.4
Skewness0.53010157
Sum861.5
Variance0.3680924
MonotonicityNot monotonic
2025-11-25T07:10:43.271038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4.716
 
8.9%
4.915
 
8.4%
4.815
 
8.4%
4.313
 
7.3%
4.411
 
6.1%
5.110
 
5.6%
4.610
 
5.6%
59
 
5.0%
5.29
 
5.0%
4.18
 
4.5%
Other values (20)63
35.2%
ValueCountFrequency (%)
3.51
 
0.6%
3.61
 
0.6%
3.72
 
1.1%
3.83
 
1.7%
3.96
3.4%
42
 
1.1%
4.18
4.5%
4.26
3.4%
4.313
7.3%
4.411
6.1%
ValueCountFrequency (%)
6.62
 
1.1%
6.41
 
0.6%
6.31
 
0.6%
6.23
1.7%
6.11
 
0.6%
5.93
1.7%
5.82
 
1.1%
5.75
2.8%
5.63
1.7%
5.56
3.4%

cd4_correction_applied
Categorical

Constant 

Quality flag: CD4 corrections applied

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0179
100.0%

Length

2025-11-25T07:10:43.316551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:43.351022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

final_comprehensive_fix_applied
Categorical

Constant 

Quality flag: Comprehensive corrections applied

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
1.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0179
100.0%

Length

2025-11-25T07:10:43.388330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:43.423170image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1179
50.0%
0179
50.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

waist_circ_unit_correction_applied
Boolean

Constant 

Quality flag: Waist circumference unit corrected

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
179 
ValueCountFrequency (%)
False179
100.0%
2025-11-25T07:10:43.452236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

sa_biomarker_standards
Categorical

Constant 

South African biomarker reference standards applied

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
1.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0179
100.0%

Length

2025-11-25T07:10:43.488441image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:43.522109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1179
50.0%
0179
50.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1179
33.3%
.179
33.3%
0179
33.3%

climate_daily_mean_temp
Real number (ℝ)

High correlation 

Daily mean temperature

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.188184
Minimum7.098
Maximum21.626
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:10:43.549624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.098
5-th percentile7.098
Q17.098
median13.599
Q319.07
95-th percentile21.626
Maximum21.626
Range14.528
Interquartile range (IQR)11.972

Descriptive statistics

Standard deviation5.4715296
Coefficient of variation (CV)0.38563987
Kurtosis-1.5230969
Mean14.188184
Median Absolute Deviation (MAD)5.471
Skewness-0.083238863
Sum2539.685
Variance29.937636
MonotonicityNot monotonic
2025-11-25T07:10:43.583662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.09850
27.9%
11.38131
17.3%
21.62629
16.2%
19.0715
 
8.4%
18.28512
 
6.7%
16.65312
 
6.7%
13.59910
 
5.6%
17.6669
 
5.0%
18.3525
 
2.8%
19.3315
 
2.8%
ValueCountFrequency (%)
7.09850
27.9%
11.38131
17.3%
13.59910
 
5.6%
16.1151
 
0.6%
16.65312
 
6.7%
17.6669
 
5.0%
18.28512
 
6.7%
18.3525
 
2.8%
19.0715
 
8.4%
19.3315
 
2.8%
ValueCountFrequency (%)
21.62629
16.2%
19.3315
 
2.8%
19.0715
8.4%
18.3525
 
2.8%
18.28512
 
6.7%
17.6669
 
5.0%
16.65312
 
6.7%
16.1151
 
0.6%
13.59910
 
5.6%
11.38131
17.3%

climate_daily_max_temp
Real number (ℝ)

High correlation 

Daily maximum temperature

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.255441
Minimum13.147
Maximum26.902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:10:43.618393image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum13.147
5-th percentile13.147
Q113.147
median19.978
Q325.656
95-th percentile26.902
Maximum26.902
Range13.755
Interquartile range (IQR)12.509

Descriptive statistics

Standard deviation5.2852604
Coefficient of variation (CV)0.2609304
Kurtosis-1.4871955
Mean20.255441
Median Absolute Deviation (MAD)5.678
Skewness-0.18123378
Sum3625.724
Variance27.933978
MonotonicityNot monotonic
2025-11-25T07:10:43.651516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
13.14750
27.9%
18.231
17.3%
26.90229
16.2%
24.3615
 
8.4%
25.65612
 
6.7%
21.60712
 
6.7%
19.97810
 
5.6%
25.9319
 
5.0%
23.4215
 
2.8%
23.8895
 
2.8%
ValueCountFrequency (%)
13.14750
27.9%
18.231
17.3%
19.97810
 
5.6%
21.60712
 
6.7%
21.7511
 
0.6%
23.4215
 
2.8%
23.8895
 
2.8%
24.3615
 
8.4%
25.65612
 
6.7%
25.9319
 
5.0%
ValueCountFrequency (%)
26.90229
16.2%
25.9319
 
5.0%
25.65612
 
6.7%
24.3615
8.4%
23.8895
 
2.8%
23.4215
 
2.8%
21.7511
 
0.6%
21.60712
 
6.7%
19.97810
 
5.6%
18.231
17.3%

climate_daily_min_temp
Real number (ℝ)

High correlation 

Daily minimum temperature

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2291899
Minimum1.468
Maximum16.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:10:43.684040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.468
5-th percentile1.468
Q11.468
median7.473
Q314.512
95-th percentile16.62
Maximum16.62
Range15.152
Interquartile range (IQR)13.044

Descriptive statistics

Standard deviation5.6820579
Coefficient of variation (CV)0.69047597
Kurtosis-1.4289073
Mean8.2291899
Median Absolute Deviation (MAD)6.005
Skewness0.21403409
Sum1473.025
Variance32.285782
MonotonicityNot monotonic
2025-11-25T07:10:43.718975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1.46850
27.9%
4.96431
17.3%
16.6229
16.2%
14.51215
 
8.4%
10.03812
 
6.7%
10.95212
 
6.7%
7.47310
 
5.6%
7.6469
 
5.0%
12.6975
 
2.8%
15.3835
 
2.8%
ValueCountFrequency (%)
1.46850
27.9%
4.96431
17.3%
7.47310
 
5.6%
7.6469
 
5.0%
10.03812
 
6.7%
10.2571
 
0.6%
10.95212
 
6.7%
12.6975
 
2.8%
14.51215
 
8.4%
15.3835
 
2.8%
ValueCountFrequency (%)
16.6229
16.2%
15.3835
 
2.8%
14.51215
8.4%
12.6975
 
2.8%
10.95212
 
6.7%
10.2571
 
0.6%
10.03812
 
6.7%
7.6469
 
5.0%
7.47310
 
5.6%
4.96431
17.3%

climate_temp_anomaly
Real number (ℝ)

High correlation 

Temperature anomaly from baseline

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0242346
Minimum2.534
Maximum9.255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:10:43.753116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.534
5-th percentile2.534
Q12.534
median5.544
Q36.554
95-th percentile7.4253
Maximum9.255
Range6.721
Interquartile range (IQR)4.02

Descriptive statistics

Standard deviation2.0897289
Coefficient of variation (CV)0.4159298
Kurtosis-1.1912532
Mean5.0242346
Median Absolute Deviation (MAD)1.678
Skewness0.067239824
Sum899.338
Variance4.3669669
MonotonicityNot monotonic
2025-11-25T07:10:43.788597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2.53450
27.9%
7.22231
17.3%
6.04729
16.2%
5.54415
 
8.4%
6.55412
 
6.7%
2.66512
 
6.7%
5.45810
 
5.6%
9.2559
 
5.0%
2.8935
 
2.8%
4.3455
 
2.8%
ValueCountFrequency (%)
2.53450
27.9%
2.66512
 
6.7%
2.8935
 
2.8%
4.3455
 
2.8%
5.45810
 
5.6%
5.541
 
0.6%
5.54415
 
8.4%
6.04729
16.2%
6.55412
 
6.7%
7.22231
17.3%
ValueCountFrequency (%)
9.2559
 
5.0%
7.22231
17.3%
6.55412
 
6.7%
6.04729
16.2%
5.54415
8.4%
5.541
 
0.6%
5.45810
 
5.6%
4.3455
 
2.8%
2.8935
 
2.8%
2.66512
 
6.7%

climate_heat_day_p90
Categorical

Constant 

Heat day indicator (>90th percentile)

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0179
100.0%

Length

2025-11-25T07:10:43.830033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:43.864169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

climate_heat_day_p95
Categorical

Constant 

Heat day indicator (>95th percentile)

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters537
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0179
100.0%

Length

2025-11-25T07:10:43.900789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:43.935444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0179
100.0%

Most occurring characters

ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number358
66.7%
Other Punctuation179
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0358
100.0%
Other Punctuation
ValueCountFrequency (%)
.179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common537
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII537
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0358
66.7%
.179
33.3%

climate_heat_stress_index
Real number (ℝ)

High correlation 

Heat stress index

Distinct11
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.58362
Minimum7.393
Maximum22.548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-11-25T07:10:43.962604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum7.393
5-th percentile7.393
Q17.393
median16.134
Q320.129
95-th percentile20.5932
Maximum22.548
Range15.155
Interquartile range (IQR)12.736

Descriptive statistics

Standard deviation5.5039646
Coefficient of variation (CV)0.37740729
Kurtosis-1.6187301
Mean14.58362
Median Absolute Deviation (MAD)4.811
Skewness-0.17802262
Sum2610.468
Variance30.293626
MonotonicityNot monotonic
2025-11-25T07:10:43.996698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7.39350
27.9%
11.32331
17.3%
20.12929
16.2%
20.37615
 
8.4%
19.34312
 
6.7%
18.07812
 
6.7%
16.13410
 
5.6%
22.5489
 
5.0%
17.4245
 
2.8%
16.3575
 
2.8%
ValueCountFrequency (%)
7.39350
27.9%
11.32331
17.3%
16.13410
 
5.6%
16.3575
 
2.8%
17.4245
 
2.8%
18.07812
 
6.7%
18.1951
 
0.6%
19.34312
 
6.7%
20.12929
16.2%
20.37615
 
8.4%
ValueCountFrequency (%)
22.5489
 
5.0%
20.37615
8.4%
20.12929
16.2%
19.34312
 
6.7%
18.1951
 
0.6%
18.07812
 
6.7%
17.4245
 
2.8%
16.3575
 
2.8%
16.13410
 
5.6%
11.32331
17.3%

climate_season
Categorical

High correlation 

Season

Distinct4
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
Winter
81 
Summer
49 
Spring
26 
Autumn
23 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters1074
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSpring
2nd rowSpring
3rd rowSpring
4th rowSpring
5th rowSpring

Common Values

ValueCountFrequency (%)
Winter81
45.3%
Summer49
27.4%
Spring26
 
14.5%
Autumn23
 
12.8%

Length

2025-11-25T07:10:44.038858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-25T07:10:44.076389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
winter81
45.3%
summer49
27.4%
spring26
 
14.5%
autumn23
 
12.8%

Most occurring characters

ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter895
83.3%
Uppercase Letter179
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r156
17.4%
n130
14.5%
e130
14.5%
m121
13.5%
i107
12.0%
t104
11.6%
u95
10.6%
p26
 
2.9%
g26
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
W81
45.3%
S75
41.9%
A23
 
12.8%

Most occurring scripts

ValueCountFrequency (%)
Latin1074
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r156
14.5%
n130
12.1%
e130
12.1%
m121
11.3%
i107
10.0%
t104
9.7%
u95
8.8%
W81
7.5%
S75
7.0%
p26
 
2.4%
Other values (2)49
 
4.6%

Interactions

2025-11-25T07:10:41.897735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:37.705966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.112328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.476605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.829718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.186038image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.663337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.017399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.374969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.748077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.116279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.544927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.924554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:37.734546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.141706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.504466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.858180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.215739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.690146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.044582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.404157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.778429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.141824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.573031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.954180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:37.764802image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.171260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.533655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.888323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.248045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.721030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.073043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.437312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.809465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.170277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.602492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.981307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:37.792790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.201601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.562016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.918317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.285106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.749501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.101763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.467506image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.840667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.198273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.632276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:42.014022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:37.853628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.231938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.590651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.946357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.316645image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.778649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.130407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.498642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.870155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.226493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.660283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:42.045896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:37.898898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.264308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.623421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.977605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.354096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.807897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.161093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.531380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.904624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.256248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.691877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:42.076099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:37.934527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.292779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.650844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.005722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.384480image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.838726image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.188618image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.561613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.933347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.282418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.719073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:42.108371image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:37.962275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.322398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.680545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.033416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.415395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.868619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.217271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.591695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.964595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.313152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.748920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:42.139260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:37.993966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.357599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.712864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.064866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.537382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.902827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.250582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.621925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.995507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.344015image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.779031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:42.172259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.027206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.391036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.745327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.098429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.571259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.934703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.280335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.653973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.023942image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.453041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.807167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:42.204584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.053756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.418956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.771697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.128020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.600330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.961118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.312260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.685192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.055354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.483808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.838936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:42.236449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.082922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.448133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:38.801293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.157149image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.632432image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:39.989846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.342654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:40.716438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.085177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.514250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-11-25T07:10:41.866884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-11-25T07:10:44.111323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
BMI (kg/m²)Potassium (mEq/L)Respiratory rate (breaths/min)body_temperature_celsiusclimate_daily_max_tempclimate_daily_mean_tempclimate_daily_min_tempclimate_heat_stress_indexclimate_seasonclimate_temp_anomalyheart_rate_bpmheight_mweight_kg
BMI (kg/m²)1.0000.1170.005-0.1260.2560.1590.1590.2020.000-0.0050.187-0.2980.910
Potassium (mEq/L)0.1171.000-0.020-0.268-0.239-0.204-0.192-0.2430.132-0.247-0.2750.1600.182
Respiratory rate (breaths/min)0.005-0.0201.0000.3270.0720.3180.318-0.1510.169-0.300-0.020-0.0140.008
body_temperature_celsius-0.126-0.2680.3271.0000.3100.3370.3370.1110.218-0.1400.294-0.097-0.142
climate_daily_max_temp0.256-0.2390.0720.3101.0000.9700.9450.9400.8310.584-0.125-0.1990.208
climate_daily_mean_temp0.159-0.2040.3180.3370.9701.0000.9900.9070.9910.491-0.0880.0550.219
climate_daily_min_temp0.159-0.1920.3180.3370.9450.9901.0000.8860.9730.436-0.0880.0550.219
climate_heat_stress_index0.202-0.243-0.1510.1110.9400.9070.8861.0000.8590.578-0.098-0.2790.098
climate_season0.0000.1320.1690.2180.8310.9910.9730.8591.0000.6500.0000.0800.302
climate_temp_anomaly-0.005-0.247-0.300-0.1400.5840.4910.4360.5780.6501.0000.020-0.204-0.105
heart_rate_bpm0.187-0.275-0.0200.294-0.125-0.088-0.088-0.0980.0000.0201.000-0.1400.123
height_m-0.2980.160-0.014-0.097-0.1990.0550.055-0.2790.080-0.204-0.1401.0000.091
weight_kg0.9100.1820.008-0.1420.2080.2190.2190.0980.302-0.1050.1230.0911.000

Missing values

2025-11-25T07:10:42.287563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-25T07:10:42.385032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-25T07:10:42.450673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

study_sourceprimary_dateBMI (kg/m²)weight_kgheight_mheart_rate_bpmRespiratory rate (breaths/min)body_temperature_celsiusPotassium (mEq/L)cd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_appliedsa_biomarker_standardsclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_temp_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_season
1983JHB_EZIN_0252020-10-1518.57703758.21.7783.020.036.53.50.01.0False1.018.28525.65610.0386.5540.00.019.343Spring
1984JHB_EZIN_0252020-10-2820.82206661.61.7272.022.036.14.60.01.0False1.018.28525.65610.0386.5540.00.019.343Spring
1985JHB_EZIN_0252020-10-2922.30815060.01.6484.019.036.34.90.01.0False1.018.28525.65610.0386.5540.00.019.343Spring
1986JHB_EZIN_0252020-11-0419.82948251.41.6174.019.036.74.30.01.0False1.018.35223.42112.6972.8930.00.017.424Spring
1987JHB_EZIN_0252020-11-0525.30762268.91.65105.020.037.14.70.01.0False1.018.35223.42112.6972.8930.00.017.424Spring
1988JHB_EZIN_0252020-12-0932.841490112.41.8596.018.036.34.90.01.0False1.019.33123.88915.3834.3450.00.016.357Summer
1989JHB_EZIN_0252020-12-1117.50639050.01.6964.019.037.04.40.01.0False1.019.33123.88915.3834.3450.00.016.357Summer
1990JHB_EZIN_0252020-12-1531.313449109.51.8750.018.036.24.90.01.0False1.019.33123.88915.3834.3450.00.016.357Summer
1991JHB_EZIN_0252021-01-0531.63371576.01.5580.019.036.44.40.01.0False1.021.62626.90216.6206.0470.00.020.129Summer
1992JHB_EZIN_0252021-01-0539.101562100.11.6070.020.036.34.10.01.0False1.021.62626.90216.6206.0470.00.020.129Summer
study_sourceprimary_dateBMI (kg/m²)weight_kgheight_mheart_rate_bpmRespiratory rate (breaths/min)body_temperature_celsiusPotassium (mEq/L)cd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_appliedsa_biomarker_standardsclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_temp_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_season
2152JHB_EZIN_0252021-06-15NaNNaNNaNNaNNaNNaN4.20.01.0False1.07.09813.1471.4682.5340.00.07.393Winter
2153JHB_EZIN_0252021-06-16NaNNaNNaNNaNNaNNaN5.10.01.0False1.07.09813.1471.4682.5340.00.07.393Winter
2154JHB_EZIN_0252021-07-01NaNNaNNaNNaNNaNNaN4.70.01.0False1.011.38118.2004.9647.2220.00.011.323Winter
2155JHB_EZIN_0252021-07-01NaNNaNNaNNaNNaNNaN3.80.01.0False1.011.38118.2004.9647.2220.00.011.323Winter
2156JHB_EZIN_0252021-05-13NaNNaNNaNNaNNaNNaN5.60.01.0False1.013.59919.9787.4735.4580.00.016.134Autumn
2157JHB_EZIN_0252021-05-20NaNNaNNaNNaNNaNNaN4.30.01.0False1.013.59919.9787.4735.4580.00.016.134Autumn
2158JHB_EZIN_0252021-05-27NaNNaNNaNNaNNaNNaN4.70.01.0False1.013.59919.9787.4735.4580.00.016.134Autumn
2159JHB_EZIN_0252021-06-08NaNNaNNaNNaNNaNNaN5.70.01.0False1.07.09813.1471.4682.5340.00.07.393Winter
2160JHB_EZIN_0252021-06-08NaNNaNNaNNaNNaNNaN5.40.01.0False1.07.09813.1471.4682.5340.00.07.393Winter
2161JHB_EZIN_0252021-06-08NaNNaNNaNNaNNaNNaN4.90.01.0False1.07.09813.1471.4682.5340.00.07.393Winter

Duplicate rows

Most frequently occurring

study_sourceprimary_dateBMI (kg/m²)weight_kgheight_mheart_rate_bpmRespiratory rate (breaths/min)body_temperature_celsiusPotassium (mEq/L)cd4_correction_appliedfinal_comprehensive_fix_appliedwaist_circ_unit_correction_appliedsa_biomarker_standardsclimate_daily_mean_tempclimate_daily_max_tempclimate_daily_min_tempclimate_temp_anomalyclimate_heat_day_p90climate_heat_day_p95climate_heat_stress_indexclimate_season# duplicates
4JHB_EZIN_0252021-06-16NaNNaNNaNNaNNaNNaN5.10.01.0False1.07.09813.1471.4682.5340.00.07.393Winter3
0JHB_EZIN_0252021-02-09NaNNaNNaNNaNNaNNaN4.10.01.0False1.019.07024.36014.5125.5440.00.020.376Summer2
1JHB_EZIN_0252021-02-16NaNNaNNaNNaNNaNNaN4.40.01.0False1.019.07024.36014.5125.5440.00.020.376Summer2
2JHB_EZIN_0252021-02-25NaNNaNNaNNaNNaNNaN5.00.01.0False1.019.07024.36014.5125.5440.00.020.376Summer2
3JHB_EZIN_0252021-05-29NaNNaNNaNNaNNaNNaN4.80.01.0False1.013.59919.9787.4735.4580.00.016.134Autumn2
5JHB_EZIN_0252021-06-22NaNNaNNaNNaNNaNNaN5.90.01.0False1.07.09813.1471.4682.5340.00.07.393Winter2
6JHB_EZIN_0252021-06-23NaNNaNNaNNaNNaNNaN4.80.01.0False1.07.09813.1471.4682.5340.00.07.393Winter2
7JHB_EZIN_0252021-06-24NaNNaNNaNNaNNaNNaN4.90.01.0False1.07.09813.1471.4682.5340.00.07.393Winter2
8JHB_EZIN_0252021-06-29NaNNaNNaNNaNNaNNaN5.50.01.0False1.07.09813.1471.4682.5340.00.07.393Winter2
9JHB_EZIN_0252021-07-01NaNNaNNaNNaNNaNNaN5.20.01.0False1.011.38118.2004.9647.2220.00.011.323Winter2